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Found 19 papers, 10 papers with code
Context Aware Local Differential Privacy
The original definition of LDP assumes that all the elements in the data domain are equally sensitive.
Federated Learning in Practice: Reflections and Projections
Federated Learning (FL) is a machine learning technique that enables multiple entities to collaboratively learn a shared model without exchanging their local data.
AirGapAgent: Protecting Privacy-Conscious Conversational Agents
The growing use of large language model (LLM)-based conversational agents to manage sensitive user data raises significant privacy concerns.
Confidential Federated Computations
Federated Learning and Analytics (FLA) have seen widespread adoption by technology platforms for processing sensitive on-device data.
Prompt Public Large Language Models to Synthesize Data for Private On-device Applications
Pre-training on public data is an effective method to improve the performance for federated learning (FL) with differential privacy (DP).
Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Production Federated Learning
While recent works have indicated that federated learning (FL) may be vulnerable to poisoning attacks by compromised clients, their real impact on production FL systems is not fully understood.
Advances and Open Problems in Federated Learning
FL embodies the principles of focused data collection and minimization, and can mitigate many of the systemic privacy risks and costs resulting from traditional, centralized machine learning and data science approaches.
Generative Models for Effective ML on Private, Decentralized Datasets
To improve real-world applications of machine learning, experienced modelers develop intuition about their datasets, their models, and how the two interact.
Context-Aware Local Differential Privacy
Local differential privacy (LDP) is a strong notion of privacy for individual users that often comes at the expense of a significant drop in utility.
Federated Evaluation of On-device Personalization
Federated learning is a distributed, on-device computation framework that enables training global models without exporting sensitive user data to servers.
Towards Federated Learning at Scale: System Design
Federated Learning is a distributed machine learning approach which enables model training on a large corpus of decentralized data.
Applied Federated Learning: Improving Google Keyboard Query Suggestions
Federated learning is a distributed form of machine learning where both the training data and model training are decentralized.
Federated Learning for Mobile Keyboard Prediction
We train a recurrent neural network language model using a distributed, on-device learning framework called federated learning for the purpose of next-word prediction in a virtual keyboard for smartphones.
Learning Differentially Private Recurrent Language Models
We demonstrate that it is possible to train large recurrent language models with user-level differential privacy guarantees with only a negligible cost in predictive accuracy.
Practical Secure Aggregation for Federated Learning on User-Held Data
Secure Aggregation protocols allow a collection of mutually distrust parties, each holding a private value, to collaboratively compute the sum of those values without revealing the values themselves.
Federated Optimization: Distributed Machine Learning for On-Device Intelligence
We refer to this setting as Federated Optimization.
Discrete Distribution Estimation under Local Privacy
The collection and analysis of user data drives improvements in the app and web ecosystems, but comes with risks to privacy.
Communication-Efficient Learning of Deep Networks from Decentralized Data
Modern mobile devices have access to a wealth of data suitable for learning models, which in turn can greatly improve the user experience on the device.
Federated Optimization:Distributed Optimization Beyond the Datacenter
We refer to this setting as Federated Optimization.
